Recently, vision-based Advanced Driver Assist Systems have gained broad
interest. In this work, we investigate free-space detection, for which we
propose to employ a Fully Convolutional Network (FCN). We show that this FCN
can be trained in a self-supervised manner and achieve similar results compared
to training on manually annotated data, thereby reducing the need for large
manually annotated training sets. To this end, our self-supervised training
relies on a stereo-vision disparity system, to automatically generate (weak)
training labels for the color-based FCN. Additionally, our self-supervised
training facilitates online training of the FCN instead of offline.
Consequently, given that the applied FCN is relatively small, the free-space
analysis becomes highly adaptive to any traffic scene that the vehicle
encounters. We have validated our algorithm using publicly available data and
on a new challenging benchmark dataset that is released with this paper.
Experiments show that the online training boosts performance with 5% when
compared to offline training, both for Fmax and AP.

本文研究了基于视觉的高级驾驶辅助系统中的自由空间检测，提出采用完全卷积网络进行自我监督训练，以降低对大型手动注释训练集的需求。通过使用立体视觉视差系统自动生成 FCN（Fully Convolutional Network）的（弱）训练标签，使得应用 FCN 能够适应任何车辆遇到的交通场景。在线训练 FCN 的结果表明，相比于离线训练，Fmax 和 AP 的性能提升了 5%。

使用自监督和在线训练的全卷积网络进行自由空间检测

Free-Space Detection with Self-Supervised and Online Trained Fully  Convolutional Networks

This paper proposes a convolutional neural network that can fuse high-level
prior for semantic image segmentation. Motivated by humans' vision recognition
system, our key design is a three-layer generative structure consisting of
high-level coding, middle-level segmentation and low-level image to introduce
global prior for semantic segmentation. Based on this structure, we proposed a
generative model called conditional variational auto-encoder (CVAE) that can
build up the links behind these three layers. These important links include an
image encoder that extracts high level info from image, a segmentation encoder
that extracts high level info from segmentation, and a hybrid decoder that
outputs semantic segmentation from the high level prior and input image. We
theoretically derive the semantic segmentation as an optimization problem
parameterized by these links. Finally, the optimization problem enables us to
take advantage of state-of-the-art fully convolutional network structure for
the implementation of the above encoders and decoder. Experimental results on
several representative datasets demonstrate our supreme performance for
semantic segmentation.

本文提出了一种融合高层次信息以进行语义图像分割的卷积神经网络，使用三层生成结构包括高级编码，中级分割和低级图像进行全局信息分割，使用条件变分自编码器建立这三层之间的连接，提高了分割的精确度和性能。

使用卷积神经网络学习高层先验知识进行语义分割

Learning High-level Prior with Convolutional Neural Networks for  Semantic Segmentation

Multiple instance learning (MIL) can reduce the need for costly annotation in
tasks such as semantic segmentation by weakening the required degree of
supervision. We propose a novel MIL formulation of multi-class semantic
segmentation learning by a fully convolutional network. In this setting, we
seek to learn a semantic segmentation model from just weak image-level labels.
The model is trained end-to-end to jointly optimize the representation while
disambiguating the pixel-image label assignment. Fully convolutional training
accepts inputs of any size, does not need object proposal pre-processing, and
offers a pixelwise loss map for selecting latent instances. Our multi-class MIL
loss exploits the further supervision given by images with multiple labels. We
evaluate this approach through preliminary experiments on the PASCAL VOC
segmentation challenge.

本文提出了一种利用多实例学习（MIL）方法进行多类语义分割的学习的新算法，只需要通过图像级标记进行训练，同时采用全卷积网络来优化细分标签的分配。实验结果表明，该方法在 PASCAL VOC 数据集的分割挑战任务中有良好的表现。